Overview

Dataset statistics

Number of variables21
Number of observations13580
Missing cells13256
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory168.0 B

Variable types

Categorical8
Numeric13

Warnings

Suburb has a high cardinality: 314 distinct values High cardinality
Address has a high cardinality: 13378 distinct values High cardinality
SellerG has a high cardinality: 268 distinct values High cardinality
Date has a high cardinality: 58 distinct values High cardinality
Rooms is highly correlated with Bedroom2High correlation
Bedroom2 is highly correlated with RoomsHigh correlation
BuildingArea has 6450 (47.5%) missing values Missing
YearBuilt has 5375 (39.6%) missing values Missing
CouncilArea has 1369 (10.1%) missing values Missing
Landsize is highly skewed (γ1 = 95.23740045) Skewed
BuildingArea is highly skewed (γ1 = 77.69154092) Skewed
Address is uniformly distributed Uniform
Car has 1026 (7.6%) zeros Zeros
Landsize has 1939 (14.3%) zeros Zeros

Reproduction

Analysis started2021-03-29 17:02:08.243854
Analysis finished2021-03-29 17:02:40.812171
Duration32.57 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Suburb
Categorical

HIGH CARDINALITY

Distinct314
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
Reservoir
 
359
Richmond
 
260
Bentleigh East
 
249
Preston
 
239
Brunswick
 
222
Other values (309)
12251 

Length

Max length18
Median length9
Mean length9.79646539
Min length3

Characters and Unicode

Total characters133036
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st rowAbbotsford
2nd rowAbbotsford
3rd rowAbbotsford
4th rowAbbotsford
5th rowAbbotsford
ValueCountFrequency (%)
Reservoir359
 
2.6%
Richmond260
 
1.9%
Bentleigh East249
 
1.8%
Preston239
 
1.8%
Brunswick222
 
1.6%
Essendon220
 
1.6%
South Yarra202
 
1.5%
Glen Iris195
 
1.4%
Hawthorn191
 
1.4%
Coburg190
 
1.4%
Other values (304)11253
82.9%
2021-03-29T14:02:41.312467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east1042
 
5.6%
north735
 
3.9%
south531
 
2.8%
west509
 
2.7%
brunswick420
 
2.3%
melbourne418
 
2.2%
bentleigh388
 
2.1%
reservoir359
 
1.9%
brighton324
 
1.7%
hawthorn310
 
1.7%
Other values (265)13616
73.0%

Most occurring characters

ValueCountFrequency (%)
e12017
 
9.0%
o11338
 
8.5%
r11177
 
8.4%
n9667
 
7.3%
a8724
 
6.6%
t8260
 
6.2%
l7303
 
5.5%
i6507
 
4.9%
s6373
 
4.8%
5072
 
3.8%
Other values (39)46598
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter109304
82.2%
Uppercase Letter18660
 
14.0%
Space Separator5072
 
3.8%

Most frequent character per category

ValueCountFrequency (%)
e12017
11.0%
o11338
10.4%
r11177
10.2%
n9667
8.8%
a8724
 
8.0%
t8260
 
7.6%
l7303
 
6.7%
i6507
 
6.0%
s6373
 
5.8%
h4409
 
4.0%
Other values (15)23529
21.5%
ValueCountFrequency (%)
B1957
 
10.5%
E1632
 
8.7%
M1491
 
8.0%
S1439
 
7.7%
H1408
 
7.5%
C1249
 
6.7%
P1221
 
6.5%
N1185
 
6.4%
W1017
 
5.5%
A932
 
5.0%
Other values (13)5129
27.5%
ValueCountFrequency (%)
5072
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin127964
96.2%
Common5072
 
3.8%

Most frequent character per script

ValueCountFrequency (%)
e12017
 
9.4%
o11338
 
8.9%
r11177
 
8.7%
n9667
 
7.6%
a8724
 
6.8%
t8260
 
6.5%
l7303
 
5.7%
i6507
 
5.1%
s6373
 
5.0%
h4409
 
3.4%
Other values (38)42189
33.0%
ValueCountFrequency (%)
5072
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII133036
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12017
 
9.0%
o11338
 
8.5%
r11177
 
8.4%
n9667
 
7.3%
a8724
 
6.6%
t8260
 
6.2%
l7303
 
5.5%
i6507
 
4.9%
s6373
 
4.8%
5072
 
3.8%
Other values (39)46598
35.0%

Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct13378
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
5 Margaret St
 
3
2 Bruce St
 
3
53 William St
 
3
14 Arthur St
 
3
36 Aberfeldie St
 
3
Other values (13373)
13565 

Length

Max length27
Median length13
Mean length13.51045655
Min length8

Characters and Unicode

Total characters183472
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13185 ?
Unique (%)97.1%

Sample

1st row85 Turner St
2nd row25 Bloomburg St
3rd row5 Charles St
4th row40 Federation La
5th row55a Park St
ValueCountFrequency (%)
5 Margaret St3
 
< 0.1%
2 Bruce St3
 
< 0.1%
53 William St3
 
< 0.1%
14 Arthur St3
 
< 0.1%
36 Aberfeldie St3
 
< 0.1%
5 Charles St3
 
< 0.1%
28 Blair St3
 
< 0.1%
13 Robinson St3
 
< 0.1%
1/1 Clarendon St3
 
< 0.1%
11 Duffy St2
 
< 0.1%
Other values (13368)13551
99.8%
2021-03-29T14:02:41.854159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st8052
 
19.7%
rd2825
 
6.9%
ct612
 
1.5%
dr447
 
1.1%
av321
 
0.8%
gr311
 
0.8%
3260
 
0.6%
4257
 
0.6%
5251
 
0.6%
7241
 
0.6%
Other values (7068)27329
66.8%

Most occurring characters

ValueCountFrequency (%)
27326
 
14.9%
t12785
 
7.0%
e9573
 
5.2%
S9002
 
4.9%
r8628
 
4.7%
a8075
 
4.4%
n7309
 
4.0%
17036
 
3.8%
o6788
 
3.7%
l6303
 
3.4%
Other values (54)80647
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter92167
50.2%
Decimal Number32266
 
17.6%
Uppercase Letter27779
 
15.1%
Space Separator27326
 
14.9%
Other Punctuation3934
 
2.1%

Most frequent character per category

ValueCountFrequency (%)
S9002
32.4%
R3536
 
12.7%
C2148
 
7.7%
B1419
 
5.1%
M1303
 
4.7%
A1299
 
4.7%
D1122
 
4.0%
P1102
 
4.0%
G1093
 
3.9%
H926
 
3.3%
Other values (16)4829
17.4%
ValueCountFrequency (%)
t12785
13.9%
e9573
10.4%
r8628
9.4%
a8075
8.8%
n7309
 
7.9%
o6788
 
7.4%
l6303
 
6.8%
d5940
 
6.4%
i5031
 
5.5%
s3384
 
3.7%
Other values (16)18351
19.9%
ValueCountFrequency (%)
17036
21.8%
24894
15.2%
33833
11.9%
43129
9.7%
52756
 
8.5%
62445
 
7.6%
72218
 
6.9%
02146
 
6.7%
82020
 
6.3%
91789
 
5.5%
ValueCountFrequency (%)
27326
100.0%
ValueCountFrequency (%)
/3934
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin119946
65.4%
Common63526
34.6%

Most frequent character per script

ValueCountFrequency (%)
t12785
 
10.7%
e9573
 
8.0%
S9002
 
7.5%
r8628
 
7.2%
a8075
 
6.7%
n7309
 
6.1%
o6788
 
5.7%
l6303
 
5.3%
d5940
 
5.0%
i5031
 
4.2%
Other values (42)40512
33.8%
ValueCountFrequency (%)
27326
43.0%
17036
 
11.1%
24894
 
7.7%
/3934
 
6.2%
33833
 
6.0%
43129
 
4.9%
52756
 
4.3%
62445
 
3.8%
72218
 
3.5%
02146
 
3.4%
Other values (2)3809
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII183472
100.0%

Most frequent character per block

ValueCountFrequency (%)
27326
 
14.9%
t12785
 
7.0%
e9573
 
5.2%
S9002
 
4.9%
r8628
 
4.7%
a8075
 
4.4%
n7309
 
4.0%
17036
 
3.8%
o6788
 
3.7%
l6303
 
3.4%
Other values (54)80647
44.0%

Rooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.937997054
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size106.2 KiB
2021-03-29T14:02:42.062159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9557479384
Coefficient of variation (CV)0.3253059553
Kurtosis0.7940679895
Mean2.937997054
Median Absolute Deviation (MAD)1
Skewness0.3764780328
Sum39898
Variance0.9134541218
MonotocityNot monotonic
2021-03-29T14:02:42.226885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
35881
43.3%
23648
26.9%
42688
19.8%
1681
 
5.0%
5596
 
4.4%
667
 
0.5%
710
 
0.1%
88
 
0.1%
101
 
< 0.1%
ValueCountFrequency (%)
1681
 
5.0%
23648
26.9%
35881
43.3%
42688
19.8%
5596
 
4.4%
ValueCountFrequency (%)
101
 
< 0.1%
88
 
0.1%
710
 
0.1%
667
 
0.5%
5596
4.4%

Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
h
9449 
u
3017 
t
1114 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13580
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowh
2nd rowh
3rd rowh
4th rowh
5th rowh
ValueCountFrequency (%)
h9449
69.6%
u3017
 
22.2%
t1114
 
8.2%
2021-03-29T14:02:42.597405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-29T14:02:42.740382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
h9449
69.6%
u3017
 
22.2%
t1114
 
8.2%

Most occurring characters

ValueCountFrequency (%)
h9449
69.6%
u3017
 
22.2%
t1114
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13580
100.0%

Most frequent character per category

ValueCountFrequency (%)
h9449
69.6%
u3017
 
22.2%
t1114
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin13580
100.0%

Most frequent character per script

ValueCountFrequency (%)
h9449
69.6%
u3017
 
22.2%
t1114
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII13580
100.0%

Most frequent character per block

ValueCountFrequency (%)
h9449
69.6%
u3017
 
22.2%
t1114
 
8.2%

Price
Real number (ℝ≥0)

Distinct2204
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1075684.079
Minimum85000
Maximum9000000
Zeros0
Zeros (%)0.0%
Memory size106.2 KiB
2021-03-29T14:02:42.881335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum85000
5-th percentile405000
Q1650000
median903000
Q31330000
95-th percentile2290050
Maximum9000000
Range8915000
Interquartile range (IQR)680000

Descriptive statistics

Standard deviation639310.7243
Coefficient of variation (CV)0.5943294472
Kurtosis9.874338886
Mean1075684.079
Median Absolute Deviation (MAD)313000
Skewness2.239624313
Sum1.46077898 × 1010
Variance4.087182022 × 1011
MonotocityNot monotonic
2021-03-29T14:02:43.103345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100000113
 
0.8%
1300000109
 
0.8%
800000109
 
0.8%
650000109
 
0.8%
600000104
 
0.8%
100000097
 
0.7%
120000097
 
0.7%
90000095
 
0.7%
70000091
 
0.7%
140000089
 
0.7%
Other values (2194)12567
92.5%
ValueCountFrequency (%)
850001
< 0.1%
1310001
< 0.1%
1450002
< 0.1%
1600001
< 0.1%
1700002
< 0.1%
ValueCountFrequency (%)
90000001
< 0.1%
80000001
< 0.1%
76500001
< 0.1%
65000001
< 0.1%
64000001
< 0.1%

Method
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
S
9022 
SP
1703 
PI
1564 
VB
1199 
SA
 
92

Length

Max length2
Median length1
Mean length1.335640648
Min length1

Characters and Unicode

Total characters18138
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowSP
4th rowPI
5th rowVB
ValueCountFrequency (%)
S9022
66.4%
SP1703
 
12.5%
PI1564
 
11.5%
VB1199
 
8.8%
SA92
 
0.7%
2021-03-29T14:02:43.495318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-29T14:02:43.637940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
s9022
66.4%
sp1703
 
12.5%
pi1564
 
11.5%
vb1199
 
8.8%
sa92
 
0.7%

Most occurring characters

ValueCountFrequency (%)
S10817
59.6%
P3267
 
18.0%
I1564
 
8.6%
V1199
 
6.6%
B1199
 
6.6%
A92
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter18138
100.0%

Most frequent character per category

ValueCountFrequency (%)
S10817
59.6%
P3267
 
18.0%
I1564
 
8.6%
V1199
 
6.6%
B1199
 
6.6%
A92
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin18138
100.0%

Most frequent character per script

ValueCountFrequency (%)
S10817
59.6%
P3267
 
18.0%
I1564
 
8.6%
V1199
 
6.6%
B1199
 
6.6%
A92
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII18138
100.0%

Most frequent character per block

ValueCountFrequency (%)
S10817
59.6%
P3267
 
18.0%
I1564
 
8.6%
V1199
 
6.6%
B1199
 
6.6%
A92
 
0.5%

SellerG
Categorical

HIGH CARDINALITY

Distinct268
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
Nelson
1565 
Jellis
1316 
hockingstuart
1167 
Barry
1011 
Ray
 
701
Other values (263)
7820 

Length

Max length23
Median length6
Mean length6.402503682
Min length1

Characters and Unicode

Total characters86946
Distinct characters57
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)0.6%

Sample

1st rowBiggin
2nd rowBiggin
3rd rowBiggin
4th rowBiggin
5th rowNelson
ValueCountFrequency (%)
Nelson1565
 
11.5%
Jellis1316
 
9.7%
hockingstuart1167
 
8.6%
Barry1011
 
7.4%
Ray701
 
5.2%
Marshall659
 
4.9%
Buxton632
 
4.7%
Biggin393
 
2.9%
Brad342
 
2.5%
Fletchers301
 
2.2%
Other values (258)5493
40.4%
2021-03-29T14:02:44.052210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nelson1565
 
11.5%
jellis1316
 
9.7%
hockingstuart1167
 
8.6%
barry1011
 
7.4%
ray701
 
5.2%
marshall659
 
4.9%
buxton632
 
4.7%
biggin393
 
2.9%
brad342
 
2.5%
woodards301
 
2.2%
Other values (255)5493
40.4%

Most occurring characters

ValueCountFrequency (%)
l7767
 
8.9%
a7196
 
8.3%
s6910
 
7.9%
r6662
 
7.7%
e6538
 
7.5%
o5706
 
6.6%
n5268
 
6.1%
i4936
 
5.7%
t4061
 
4.7%
h2851
 
3.3%
Other values (47)29051
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter72731
83.7%
Uppercase Letter13923
 
16.0%
Other Punctuation178
 
0.2%
Decimal Number114
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
B2654
19.1%
N1852
13.3%
J1617
11.6%
R1303
9.4%
M1294
9.3%
G674
 
4.8%
W584
 
4.2%
H478
 
3.4%
S413
 
3.0%
C403
 
2.9%
Other values (15)2651
19.0%
ValueCountFrequency (%)
l7767
10.7%
a7196
9.9%
s6910
9.5%
r6662
9.2%
e6538
9.0%
o5706
 
7.8%
n5268
 
7.2%
i4936
 
6.8%
t4061
 
5.6%
h2851
 
3.9%
Other values (15)14836
20.4%
ValueCountFrequency (%)
'106
59.6%
&32
 
18.0%
.29
 
16.3%
/9
 
5.1%
@2
 
1.1%
ValueCountFrequency (%)
257
50.0%
157
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin86654
99.7%
Common292
 
0.3%

Most frequent character per script

ValueCountFrequency (%)
l7767
 
9.0%
a7196
 
8.3%
s6910
 
8.0%
r6662
 
7.7%
e6538
 
7.5%
o5706
 
6.6%
n5268
 
6.1%
i4936
 
5.7%
t4061
 
4.7%
h2851
 
3.3%
Other values (40)28759
33.2%
ValueCountFrequency (%)
'106
36.3%
257
19.5%
157
19.5%
&32
 
11.0%
.29
 
9.9%
/9
 
3.1%
@2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII86946
100.0%

Most frequent character per block

ValueCountFrequency (%)
l7767
 
8.9%
a7196
 
8.3%
s6910
 
7.9%
r6662
 
7.7%
e6538
 
7.5%
o5706
 
6.6%
n5268
 
6.1%
i4936
 
5.7%
t4061
 
4.7%
h2851
 
3.3%
Other values (47)29051
33.4%

Date
Categorical

HIGH CARDINALITY

Distinct58
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
27/05/2017
 
473
3/06/2017
 
395
12/08/2017
 
387
17/06/2017
 
374
27/11/2016
 
362
Other values (53)
11589 

Length

Max length10
Median length10
Mean length9.724815906
Min length9

Characters and Unicode

Total characters132063
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3/12/2016
2nd row4/02/2016
3rd row4/03/2017
4th row4/03/2017
5th row4/06/2016
ValueCountFrequency (%)
27/05/2017473
 
3.5%
3/06/2017395
 
2.9%
12/08/2017387
 
2.8%
17/06/2017374
 
2.8%
27/11/2016362
 
2.7%
29/07/2017341
 
2.5%
4/03/2017337
 
2.5%
25/02/2017333
 
2.5%
24/06/2017329
 
2.4%
10/12/2016319
 
2.3%
Other values (48)9930
73.1%
2021-03-29T14:02:44.500399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
27/05/2017473
 
3.5%
3/06/2017395
 
2.9%
12/08/2017387
 
2.8%
17/06/2017374
 
2.8%
27/11/2016362
 
2.7%
29/07/2017341
 
2.5%
4/03/2017337
 
2.5%
25/02/2017333
 
2.5%
24/06/2017329
 
2.4%
10/12/2016319
 
2.3%
Other values (48)9930
73.1%

Most occurring characters

ValueCountFrequency (%)
/27160
20.6%
026427
20.0%
122198
16.8%
221040
15.9%
711327
8.6%
69451
 
7.2%
83453
 
2.6%
93075
 
2.3%
52954
 
2.2%
32690
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number104903
79.4%
Other Punctuation27160
 
20.6%

Most frequent character per category

ValueCountFrequency (%)
026427
25.2%
122198
21.2%
221040
20.1%
711327
10.8%
69451
 
9.0%
83453
 
3.3%
93075
 
2.9%
52954
 
2.8%
32690
 
2.6%
42288
 
2.2%
ValueCountFrequency (%)
/27160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common132063
100.0%

Most frequent character per script

ValueCountFrequency (%)
/27160
20.6%
026427
20.0%
122198
16.8%
221040
15.9%
711327
8.6%
69451
 
7.2%
83453
 
2.6%
93075
 
2.3%
52954
 
2.2%
32690
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII132063
100.0%

Most frequent character per block

ValueCountFrequency (%)
/27160
20.6%
026427
20.0%
122198
16.8%
221040
15.9%
711327
8.6%
69451
 
7.2%
83453
 
2.6%
93075
 
2.3%
52954
 
2.2%
32690
 
2.0%

Distance
Real number (ℝ≥0)

Distinct202
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.13777614
Minimum0
Maximum48.1
Zeros6
Zeros (%)< 0.1%
Memory size106.2 KiB
2021-03-29T14:02:44.716699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q16.1
median9.2
Q313
95-th percentile20.6
Maximum48.1
Range48.1
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation5.868724943
Coefficient of variation (CV)0.5788966792
Kurtosis5.260001109
Mean10.13777614
Median Absolute Deviation (MAD)3.35
Skewness1.676937083
Sum137671
Variance34.44193246
MonotocityNot monotonic
2021-03-29T14:02:44.968439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.2739
 
5.4%
9.2367
 
2.7%
13.9324
 
2.4%
7.8306
 
2.3%
4.6263
 
1.9%
13252
 
1.9%
8248
 
1.8%
5.2248
 
1.8%
13.8237
 
1.7%
2.6235
 
1.7%
Other values (192)10361
76.3%
ValueCountFrequency (%)
06
 
< 0.1%
0.78
 
0.1%
1.233
0.2%
1.35
 
< 0.1%
1.517
0.1%
ValueCountFrequency (%)
48.11
 
< 0.1%
47.41
 
< 0.1%
47.33
 
< 0.1%
45.99
0.1%
45.21
 
< 0.1%

Postcode
Real number (ℝ≥0)

Distinct198
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3105.301915
Minimum3000
Maximum3977
Zeros0
Zeros (%)0.0%
Memory size106.2 KiB
2021-03-29T14:02:45.211512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile3013
Q13044
median3084
Q33148
95-th percentile3204
Maximum3977
Range977
Interquartile range (IQR)104

Descriptive statistics

Standard deviation90.67696409
Coefficient of variation (CV)0.02920069178
Kurtosis29.15686787
Mean3105.301915
Median Absolute Deviation (MAD)50
Skewness4.076152215
Sum42170000
Variance8222.311816
MonotocityNot monotonic
2021-03-29T14:02:45.461941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3073359
 
2.6%
3020306
 
2.3%
3121292
 
2.2%
3040290
 
2.1%
3046284
 
2.1%
3165249
 
1.8%
3058246
 
1.8%
3163245
 
1.8%
3012242
 
1.8%
3072239
 
1.8%
Other values (188)10828
79.7%
ValueCountFrequency (%)
300046
0.3%
300222
0.2%
300331
0.2%
300641
0.3%
30083
 
< 0.1%
ValueCountFrequency (%)
39778
0.1%
39764
< 0.1%
39106
< 0.1%
38103
 
< 0.1%
38091
 
< 0.1%

Bedroom2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.914727541
Minimum0
Maximum20
Zeros16
Zeros (%)0.1%
Memory size106.2 KiB
2021-03-29T14:02:45.677397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9659210617
Coefficient of variation (CV)0.33139326
Kurtosis8.074963808
Mean2.914727541
Median Absolute Deviation (MAD)1
Skewness0.7740822106
Sum39582
Variance0.9330034975
MonotocityNot monotonic
2021-03-29T14:02:45.859529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
35896
43.4%
23737
27.5%
42601
19.2%
1691
 
5.1%
5556
 
4.1%
663
 
0.5%
016
 
0.1%
710
 
0.1%
85
 
< 0.1%
93
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
016
 
0.1%
1691
 
5.1%
23737
27.5%
35896
43.4%
42601
19.2%
ValueCountFrequency (%)
201
 
< 0.1%
101
 
< 0.1%
93
 
< 0.1%
85
< 0.1%
710
0.1%

Bathroom
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.534241532
Minimum0
Maximum8
Zeros34
Zeros (%)0.3%
Memory size106.2 KiB
2021-03-29T14:02:46.050216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6917117225
Coefficient of variation (CV)0.4508493012
Kurtosis3.594973134
Mean1.534241532
Median Absolute Deviation (MAD)0
Skewness1.377405972
Sum20835
Variance0.478465107
MonotocityNot monotonic
2021-03-29T14:02:46.197672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
17512
55.3%
24974
36.6%
3917
 
6.8%
4106
 
0.8%
034
 
0.3%
528
 
0.2%
65
 
< 0.1%
82
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
034
 
0.3%
17512
55.3%
24974
36.6%
3917
 
6.8%
4106
 
0.8%
ValueCountFrequency (%)
82
 
< 0.1%
72
 
< 0.1%
65
 
< 0.1%
528
 
0.2%
4106
0.8%

Car
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing62
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.610075455
Minimum0
Maximum10
Zeros1026
Zeros (%)7.6%
Memory size106.2 KiB
2021-03-29T14:02:46.379125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9626335192
Coefficient of variation (CV)0.5978809976
Kurtosis5.193182788
Mean1.610075455
Median Absolute Deviation (MAD)1
Skewness1.369675926
Sum21765
Variance0.9266632924
MonotocityNot monotonic
2021-03-29T14:02:46.560889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
25591
41.2%
15509
40.6%
01026
 
7.6%
3748
 
5.5%
4506
 
3.7%
563
 
0.5%
654
 
0.4%
89
 
0.1%
78
 
0.1%
103
 
< 0.1%
(Missing)62
 
0.5%
ValueCountFrequency (%)
01026
 
7.6%
15509
40.6%
25591
41.2%
3748
 
5.5%
4506
 
3.7%
ValueCountFrequency (%)
103
 
< 0.1%
91
 
< 0.1%
89
 
0.1%
78
 
0.1%
654
0.4%

Landsize
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct1448
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean558.4161267
Minimum0
Maximum433014
Zeros1939
Zeros (%)14.3%
Memory size106.2 KiB
2021-03-29T14:02:46.768709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1177
median440
Q3651
95-th percentile995
Maximum433014
Range433014
Interquartile range (IQR)474

Descriptive statistics

Standard deviation3990.669241
Coefficient of variation (CV)7.146407581
Kurtosis10180.34683
Mean558.4161267
Median Absolute Deviation (MAD)236
Skewness95.23740045
Sum7583291
Variance15925440.99
MonotocityNot monotonic
2021-03-29T14:02:46.961296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01939
 
14.3%
650103
 
0.8%
69771
 
0.5%
70048
 
0.4%
58547
 
0.3%
53442
 
0.3%
59039
 
0.3%
69636
 
0.3%
64936
 
0.3%
60435
 
0.3%
Other values (1438)11184
82.4%
ValueCountFrequency (%)
01939
14.3%
12
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
4330141
< 0.1%
760001
< 0.1%
751001
< 0.1%
445001
< 0.1%
414001
< 0.1%

BuildingArea
Real number (ℝ≥0)

MISSING
SKEWED

Distinct602
Distinct (%)8.4%
Missing6450
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean151.9676499
Minimum0
Maximum44515
Zeros17
Zeros (%)0.1%
Memory size106.2 KiB
2021-03-29T14:02:47.197203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q193
median126
Q3174
95-th percentile294
Maximum44515
Range44515
Interquartile range (IQR)81

Descriptive statistics

Standard deviation541.0145376
Coefficient of variation (CV)3.560063856
Kurtosis6347.802222
Mean151.9676499
Median Absolute Deviation (MAD)39
Skewness77.69154092
Sum1083529.344
Variance292696.7299
MonotocityNot monotonic
2021-03-29T14:02:47.662281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120114
 
0.8%
11089
 
0.7%
10088
 
0.6%
13084
 
0.6%
11577
 
0.6%
15074
 
0.5%
10466
 
0.5%
9065
 
0.5%
14064
 
0.5%
8563
 
0.5%
Other values (592)6346
46.7%
(Missing)6450
47.5%
ValueCountFrequency (%)
017
0.1%
111
0.1%
216
0.1%
320
0.1%
44
 
< 0.1%
ValueCountFrequency (%)
445151
< 0.1%
67911
< 0.1%
35581
< 0.1%
31121
< 0.1%
15611
< 0.1%

YearBuilt
Real number (ℝ≥0)

MISSING

Distinct144
Distinct (%)1.8%
Missing5375
Missing (%)39.6%
Infinite0
Infinite (%)0.0%
Mean1964.684217
Minimum1196
Maximum2018
Zeros0
Zeros (%)0.0%
Memory size106.2 KiB
2021-03-29T14:02:47.894859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1196
5-th percentile1900
Q11940
median1970
Q31999
95-th percentile2012
Maximum2018
Range822
Interquartile range (IQR)59

Descriptive statistics

Standard deviation37.27376222
Coefficient of variation (CV)0.01897188459
Kurtosis21.22603222
Mean1964.684217
Median Absolute Deviation (MAD)30
Skewness-1.54127876
Sum16120234
Variance1389.33335
MonotocityNot monotonic
2021-03-29T14:02:48.121066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970866
 
6.4%
1960725
 
5.3%
1950580
 
4.3%
1900341
 
2.5%
1980338
 
2.5%
2000300
 
2.2%
1920280
 
2.1%
1930274
 
2.0%
1910240
 
1.8%
1940238
 
1.8%
Other values (134)4023
29.6%
(Missing)5375
39.6%
ValueCountFrequency (%)
11961
 
< 0.1%
18301
 
< 0.1%
18504
< 0.1%
18541
 
< 0.1%
18561
 
< 0.1%
ValueCountFrequency (%)
20181
 
< 0.1%
201718
 
0.1%
201658
0.4%
201565
0.5%
2014100
0.7%

CouncilArea
Categorical

MISSING

Distinct33
Distinct (%)0.3%
Missing1369
Missing (%)10.1%
Memory size106.2 KiB
Moreland
1163 
Boroondara
1160 
Moonee Valley
997 
Darebin
934 
Glen Eira
848 
Other values (28)
7109 

Length

Max length17
Median length9
Mean length9.069281795
Min length4

Characters and Unicode

Total characters110745
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowYarra
2nd rowYarra
3rd rowYarra
4th rowYarra
5th rowYarra
ValueCountFrequency (%)
Moreland1163
 
8.6%
Boroondara1160
 
8.5%
Moonee Valley997
 
7.3%
Darebin934
 
6.9%
Glen Eira848
 
6.2%
Stonnington719
 
5.3%
Maribyrnong692
 
5.1%
Yarra647
 
4.8%
Port Phillip628
 
4.6%
Banyule594
 
4.4%
Other values (23)3829
28.2%
(Missing)1369
 
10.1%
2021-03-29T14:02:48.633306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
moreland1163
 
7.7%
boroondara1160
 
7.6%
valley997
 
6.6%
moonee997
 
6.6%
darebin934
 
6.1%
eira848
 
5.6%
glen848
 
5.6%
stonnington719
 
4.7%
maribyrnong692
 
4.6%
yarra665
 
4.4%
Other values (28)6172
40.6%

Most occurring characters

ValueCountFrequency (%)
n13579
12.3%
o11999
 
10.8%
a11840
 
10.7%
r10051
 
9.1%
e9358
 
8.5%
l6633
 
6.0%
i6440
 
5.8%
M4120
 
3.7%
y3330
 
3.0%
B3101
 
2.8%
Other values (29)30294
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter92566
83.6%
Uppercase Letter15195
 
13.7%
Space Separator2984
 
2.7%

Most frequent character per category

ValueCountFrequency (%)
n13579
14.7%
o11999
13.0%
a11840
12.8%
r10051
10.9%
e9358
10.1%
l6633
7.2%
i6440
7.0%
y3330
 
3.6%
t3082
 
3.3%
d3045
 
3.3%
Other values (11)13209
14.3%
ValueCountFrequency (%)
M4120
27.1%
B3101
20.4%
P1256
 
8.3%
V997
 
6.6%
D986
 
6.5%
G900
 
5.9%
E848
 
5.6%
S719
 
4.7%
Y665
 
4.4%
H598
 
3.9%
Other values (7)1005
 
6.6%
ValueCountFrequency (%)
2984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin107761
97.3%
Common2984
 
2.7%

Most frequent character per script

ValueCountFrequency (%)
n13579
12.6%
o11999
11.1%
a11840
11.0%
r10051
 
9.3%
e9358
 
8.7%
l6633
 
6.2%
i6440
 
6.0%
M4120
 
3.8%
y3330
 
3.1%
B3101
 
2.9%
Other values (28)27310
25.3%
ValueCountFrequency (%)
2984
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII110745
100.0%

Most frequent character per block

ValueCountFrequency (%)
n13579
12.3%
o11999
 
10.8%
a11840
 
10.7%
r10051
 
9.1%
e9358
 
8.5%
l6633
 
6.0%
i6440
 
5.8%
M4120
 
3.7%
y3330
 
3.0%
B3101
 
2.8%
Other values (29)30294
27.4%

Lattitude
Real number (ℝ)

Distinct6503
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.80920273
Minimum-38.18255
Maximum-37.40853
Zeros0
Zeros (%)0.0%
Memory size106.2 KiB
2021-03-29T14:02:48.863529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-38.18255
5-th percentile-37.9348
Q1-37.8568225
median-37.802355
Q3-37.7564
95-th percentile-37.6989385
Maximum-37.40853
Range0.77402
Interquartile range (IQR)0.1004225

Descriptive statistics

Standard deviation0.0792598226
Coefficient of variation (CV)-0.002096310339
Kurtosis1.573252695
Mean-37.80920273
Median Absolute Deviation (MAD)0.050455
Skewness-0.4266949343
Sum-513448.9731
Variance0.006282119479
MonotocityNot monotonic
2021-03-29T14:02:49.104300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-37.836121
 
0.2%
-37.796916
 
0.1%
-37.842416
 
0.1%
-37.760914
 
0.1%
-37.819813
 
0.1%
-37.857313
 
0.1%
-37.763413
 
0.1%
-37.841413
 
0.1%
-37.767913
 
0.1%
-37.816113
 
0.1%
Other values (6493)13435
98.9%
ValueCountFrequency (%)
-38.182551
< 0.1%
-38.174881
< 0.1%
-38.168021
< 0.1%
-38.167621
< 0.1%
-38.166241
< 0.1%
ValueCountFrequency (%)
-37.408531
< 0.1%
-37.453921
< 0.1%
-37.457091
< 0.1%
-37.483811
< 0.1%
-37.487011
< 0.1%

Longtitude
Real number (ℝ≥0)

Distinct7063
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.9952162
Minimum144.43181
Maximum145.52635
Zeros0
Zeros (%)0.0%
Memory size106.2 KiB
2021-03-29T14:02:49.407908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum144.43181
5-th percentile144.835785
Q1144.9296
median145.0001
Q3145.058305
95-th percentile145.153631
Maximum145.52635
Range1.09454
Interquartile range (IQR)0.128705

Descriptive statistics

Standard deviation0.1039155614
Coefficient of variation (CV)0.0007166826888
Kurtosis1.758615585
Mean144.9952162
Median Absolute Deviation (MAD)0.063415
Skewness-0.2109908954
Sum1969035.036
Variance0.0107984439
MonotocityNot monotonic
2021-03-29T14:02:49.665325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144.996617
 
0.1%
145.010415
 
0.1%
144.98514
 
0.1%
145.000113
 
0.1%
144.99113
 
0.1%
144.99712
 
0.1%
145.024312
 
0.1%
145.011612
 
0.1%
145.02112
 
0.1%
144.987312
 
0.1%
Other values (7053)13448
99.0%
ValueCountFrequency (%)
144.431811
< 0.1%
144.485711
< 0.1%
144.542371
< 0.1%
144.545321
< 0.1%
144.551061
< 0.1%
ValueCountFrequency (%)
145.526351
< 0.1%
145.482731
< 0.1%
145.470521
< 0.1%
145.453761
< 0.1%
145.44531
< 0.1%

Regionname
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size106.2 KiB
Southern Metropolitan
4695 
Northern Metropolitan
3890 
Western Metropolitan
2948 
Eastern Metropolitan
1471 
South-Eastern Metropolitan
 
450
Other values (3)
 
126

Length

Max length26
Median length21
Mean length20.79690722
Min length16

Characters and Unicode

Total characters282422
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthern Metropolitan
2nd rowNorthern Metropolitan
3rd rowNorthern Metropolitan
4th rowNorthern Metropolitan
5th rowNorthern Metropolitan
ValueCountFrequency (%)
Southern Metropolitan4695
34.6%
Northern Metropolitan3890
28.6%
Western Metropolitan2948
21.7%
Eastern Metropolitan1471
 
10.8%
South-Eastern Metropolitan450
 
3.3%
Eastern Victoria53
 
0.4%
Northern Victoria41
 
0.3%
Western Victoria32
 
0.2%
2021-03-29T14:02:50.177029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-29T14:02:50.372256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
metropolitan13454
49.5%
southern4695
 
17.3%
northern3931
 
14.5%
western2980
 
11.0%
eastern1524
 
5.6%
south-eastern450
 
1.7%
victoria126
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t41064
14.5%
o36110
12.8%
r31091
11.0%
e30014
10.6%
n27034
9.6%
a15554
 
5.5%
i13706
 
4.9%
13580
 
4.8%
M13454
 
4.8%
p13454
 
4.8%
Other values (11)47361
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter240782
85.3%
Uppercase Letter27610
 
9.8%
Space Separator13580
 
4.8%
Dash Punctuation450
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
t41064
17.1%
o36110
15.0%
r31091
12.9%
e30014
12.5%
n27034
11.2%
a15554
 
6.5%
i13706
 
5.7%
p13454
 
5.6%
l13454
 
5.6%
h9076
 
3.8%
Other values (3)10225
 
4.2%
ValueCountFrequency (%)
M13454
48.7%
S5145
 
18.6%
N3931
 
14.2%
W2980
 
10.8%
E1974
 
7.1%
V126
 
0.5%
ValueCountFrequency (%)
13580
100.0%
ValueCountFrequency (%)
-450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin268392
95.0%
Common14030
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
t41064
15.3%
o36110
13.5%
r31091
11.6%
e30014
11.2%
n27034
10.1%
a15554
 
5.8%
i13706
 
5.1%
M13454
 
5.0%
p13454
 
5.0%
l13454
 
5.0%
Other values (9)33457
12.5%
ValueCountFrequency (%)
13580
96.8%
-450
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII282422
100.0%

Most frequent character per block

ValueCountFrequency (%)
t41064
14.5%
o36110
12.8%
r31091
11.0%
e30014
10.6%
n27034
9.6%
a15554
 
5.5%
i13706
 
4.9%
13580
 
4.8%
M13454
 
4.8%
p13454
 
4.8%
Other values (11)47361
16.8%

Propertycount
Real number (ℝ≥0)

Distinct311
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7454.417378
Minimum249
Maximum21650
Zeros0
Zeros (%)0.0%
Memory size106.2 KiB
2021-03-29T14:02:50.681127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum249
5-th percentile2185
Q14380
median6555
Q310331
95-th percentile14949
Maximum21650
Range21401
Interquartile range (IQR)5951

Descriptive statistics

Standard deviation4378.581772
Coefficient of variation (CV)0.5873808172
Kurtosis1.217820011
Mean7454.417378
Median Absolute Deviation (MAD)2695.5
Skewness1.069339349
Sum101230988
Variance19171978.33
MonotocityNot monotonic
2021-03-29T14:02:50.885784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21650359
 
2.6%
8870298
 
2.2%
14949260
 
1.9%
10969249
 
1.8%
14577239
 
1.8%
11918222
 
1.6%
9264220
 
1.6%
14887202
 
1.5%
10412195
 
1.4%
11308191
 
1.4%
Other values (301)11145
82.1%
ValueCountFrequency (%)
2491
 
< 0.1%
3896
< 0.1%
3942
 
< 0.1%
4387
0.1%
4572
 
< 0.1%
ValueCountFrequency (%)
21650359
2.6%
1749646
 
0.3%
173843
 
< 0.1%
1709313
 
0.1%
1705524
 
0.2%

Interactions

2021-03-29T14:02:13.411375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:13.569483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:13.714485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:13.868061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:14.027521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:14.191608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:14.339048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:14.583767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:14.742543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:14.891788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:15.059722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:15.208189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:15.364080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:15.505911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:15.659061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:15.808332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:15.973859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:16.127681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:16.276285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:16.418993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:16.568443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:16.712365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:16.880069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:17.035208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:17.184896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:17.330081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:17.482854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:17.636133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:17.803044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:17.967654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:18.115748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:18.265550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:18.412842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:18.563055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:18.827292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:18.984562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:19.133193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:19.287578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:19.439014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:19.596127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:19.767473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:19.930821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:20.085883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:20.233293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:20.386348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:20.533157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:20.703959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:20.864217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:21.015804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:21.186368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:21.357780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:21.526421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:21.700589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:21.886650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:22.058145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:22.220103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:22.367882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:22.515415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:22.705257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:22.880606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:23.049570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:23.212030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:23.384001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:23.554875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:23.721633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:23.902246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:24.185481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:24.354723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:24.513850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:24.682235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:24.867017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:25.032065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:25.204483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:25.356195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:25.503000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:25.656304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:25.807846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:25.971891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:26.130362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:26.279926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:26.439466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:26.591467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:26.759208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:26.911566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:27.063492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:27.206372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:27.361687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:27.507478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:27.660583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:27.816682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:27.978377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:28.121243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:28.272110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:28.436736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:28.603835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:28.758603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:28.902344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:29.061208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:29.200717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:29.352002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:29.499019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:29.640258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:29.796576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:29.941678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:30.087997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:30.243368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:30.553755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:30.699481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:30.847061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:30.987730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:31.130369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:31.273199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:31.418966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:31.568259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:31.722515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:31.871740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:32.026367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:32.179245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:32.342303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:32.490608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:32.635972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:32.809563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:32.983817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:33.161619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:33.335278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:33.523916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:33.707578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:33.882235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:34.055340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:34.224797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:34.403771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:34.582014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:34.755875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:34.908646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:35.065648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:35.219832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:35.384147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:35.557182image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:35.722851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:35.883644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:36.036071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:36.187216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:36.335935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:36.511914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:36.672078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:36.814631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:36.964985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:37.112845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:37.267353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:37.430727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:37.591204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:37.738632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:37.889372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:38.033887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:38.186032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-29T14:02:38.525075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-29T14:02:51.089634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-29T14:02:51.417233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-29T14:02:51.704028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-29T14:02:51.998272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-29T14:02:52.295129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-29T14:02:39.003700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-29T14:02:39.697968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-29T14:02:40.299827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-29T14:02:40.510079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
0Abbotsford85 Turner St2h1480000.0SBiggin3/12/20162.53067.02.01.01.0202.0NaNNaNYarra-37.7996144.9984Northern Metropolitan4019.0
1Abbotsford25 Bloomburg St2h1035000.0SBiggin4/02/20162.53067.02.01.00.0156.079.01900.0Yarra-37.8079144.9934Northern Metropolitan4019.0
2Abbotsford5 Charles St3h1465000.0SPBiggin4/03/20172.53067.03.02.00.0134.0150.01900.0Yarra-37.8093144.9944Northern Metropolitan4019.0
3Abbotsford40 Federation La3h850000.0PIBiggin4/03/20172.53067.03.02.01.094.0NaNNaNYarra-37.7969144.9969Northern Metropolitan4019.0
4Abbotsford55a Park St4h1600000.0VBNelson4/06/20162.53067.03.01.02.0120.0142.02014.0Yarra-37.8072144.9941Northern Metropolitan4019.0
5Abbotsford129 Charles St2h941000.0SJellis7/05/20162.53067.02.01.00.0181.0NaNNaNYarra-37.8041144.9953Northern Metropolitan4019.0
6Abbotsford124 Yarra St3h1876000.0SNelson7/05/20162.53067.04.02.00.0245.0210.01910.0Yarra-37.8024144.9993Northern Metropolitan4019.0
7Abbotsford98 Charles St2h1636000.0SNelson8/10/20162.53067.02.01.02.0256.0107.01890.0Yarra-37.8060144.9954Northern Metropolitan4019.0
8Abbotsford6/241 Nicholson St1u300000.0SBiggin8/10/20162.53067.01.01.01.00.0NaNNaNYarra-37.8008144.9973Northern Metropolitan4019.0
9Abbotsford10 Valiant St2h1097000.0SBiggin8/10/20162.53067.03.01.02.0220.075.01900.0Yarra-37.8010144.9989Northern Metropolitan4019.0

Last rows

SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeBuildingAreaYearBuiltCouncilAreaLattitudeLongtitudeRegionnamePropertycount
13570Wantirna South34 Fewster Dr3h970000.0SBarry26/08/201714.73152.03.02.02.0674.0NaNNaNNaN-37.88360145.22805Eastern Metropolitan7082.0
13571Wantirna South15 Mara Cl4h1330000.0SBarry26/08/201714.73152.04.02.02.0717.0191.01980.0NaN-37.86887145.22116Eastern Metropolitan7082.0
13572Watsonia76 Kenmare St2h650000.0PIMorrison26/08/201714.53087.02.01.01.0210.079.02006.0NaN-37.70657145.07878Northern Metropolitan2329.0
13573Werribee5 Nuragi Ct4h635000.0Shockingstuart26/08/201714.73030.04.02.01.0662.0172.01980.0NaN-37.89327144.64789Western Metropolitan16166.0
13574Westmeadows9 Black St3h582000.0SRed26/08/201716.53049.03.02.02.0256.0NaNNaNNaN-37.67917144.89390Northern Metropolitan2474.0
13575Wheelers Hill12 Strada Cr4h1245000.0SBarry26/08/201716.73150.04.02.02.0652.0NaN1981.0NaN-37.90562145.16761South-Eastern Metropolitan7392.0
13576Williamstown77 Merrett Dr3h1031000.0SPWilliams26/08/20176.83016.03.02.02.0333.0133.01995.0NaN-37.85927144.87904Western Metropolitan6380.0
13577Williamstown83 Power St3h1170000.0SRaine26/08/20176.83016.03.02.04.0436.0NaN1997.0NaN-37.85274144.88738Western Metropolitan6380.0
13578Williamstown96 Verdon St4h2500000.0PISweeney26/08/20176.83016.04.01.05.0866.0157.01920.0NaN-37.85908144.89299Western Metropolitan6380.0
13579Yarraville6 Agnes St4h1285000.0SPVillage26/08/20176.33013.04.01.01.0362.0112.01920.0NaN-37.81188144.88449Western Metropolitan6543.0